Papers by Meiyun Wang
Lost in the Distance: Large Language Models Struggle to Capture Long-Distance Relational Knowledge (2025.findings-naacl)
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| Challenge: | Recent large language models have demonstrated impressive capabilities in handling long contexts . however, as context length increases, LLMs struggle more with filtering out irrelevant information . |
| Approach: | They propose to use unrelated sentences to capture relational knowledge over long contexts . they find that LLMs can handle edge noise with little impact, but can reason about distant relationships . |
| Outcome: | The proposed model can handle edge noise with little impact, but its ability to reason about distant relationships declines as the noise grows. |
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction (2024.findings-acl)
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| Challenge: | Recent work has focused on the performance of Large Language Models (LLMs) but the finance sector is relying on time-series data for complex forecasting tasks. |
| Approach: | They propose a framework that employs Sequential Knowledge-Guided Prompting to identify factors that influence stock movements using LLMs. |
| Outcome: | The proposed framework outperforms existing methods and is effective in time-series forecasting. |
The CRECIL Corpus: a New Dataset for Extraction of Relations between Characters in Chinese Multi-party Dialogues (2022.lrec-1)
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Yuru Jiang, Yang Xu, Yuhang Zhan, Weikai He, Yilin Wang, Zixuan Xi, Meiyun Wang, Xinyu Li, Yu Li, Yanchao Yu
| Challenge: | Existing datasets focus on relation extraction between two entities in one sentence, and some focus on cross-sentence relationships. |
| Approach: | They propose to use a Chinese multi-party dialogue dataset for automatic extraction of dialogue-based character relationships. |
| Outcome: | The proposed dataset extracts relationships between 140 entities on the CRECIL corpus and another existing relation extraction corpus. |